Psychometric and Genetic Assessments of Substance Use

药物使用的心理测量和基因评估

• 批准号: 6952887
• 负责人: MICHAEL CHURTON NEALE
• 金额: $ 37.6万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-30 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6952887
• 关键词: Internet; behavioral /social science research tag; behavioral genetics; biomarker; clinical research; comorbidity; computer program /software; data management; gene environment interaction; longitudinal human study; mathematical model; model design /development; phenotype; psychometrics; relapse /recurrence; statistics /biometry; substance abuse related disorder

DESCRIPTION (provided by applicant): This project aims to develop a series of novel approaches to phenotyping drug use and abuse. The general scheme is to develop statistical models from theory, implement them in user friendly software, and examine their statistical properties. Those models that perform sufficiently well will be applied to one or more sets of data to bring new insight into the assessment of substance use. The first goal is to extend of models for factorial invariance, which form the basis of testing for differences between groups. The primary extension will be to allow testing of invariance not merely between distinct groups, but also within groups that vary with respect to continuous variables such as age. This approach will be applied to confirmatory factor analysis, to latent class analysis, and to models that represent mixtures of both factors and latent classes, and will be able to handle binary, ordinal and continuous observed variables. The method should prove valuable in assessing whether substance abuse patterns in the population represent continuous variation in liability or whether distinct groups exist. The second goal is to extend models for regime switching in the context of growth curve and other factor mixture models. This aim is intended to provide a better model for data that involve onset and offset of substance use, and to assist in uncovering heterogeneity. Third, we will develop methods for the analysis of certain forms of partially anonymized data such as those involving randomized response. These methods will be compared for their performance at detecting relationships with predictors, sequellae and correlates of partially randomized data, including resemblance between relatives and outcomes. All model development will be designed to permit the analysis and exploitation of data collected from relatives, and will include models for data on genetic markers, for both linkage and association studies. Applied data analyses will yield substantive results, guide model development, and test for robustness. An array of cross-sectional, longitudinal, and genetically informative datasets will be assembled and analyzed.

描述（由申请人提供）： 该项目旨在开发一系列对药物使用和滥用进行表型分析的新方法。总体方案是从理论出发开发统计模型，在用户友好的软件中实现它们，并检查它们的统计特性。那些表现足够好的模型将应用于一组或多组数据，以便为物质使用评估带来新的见解。第一个目标是扩展阶乘不变性模型，该模型构成了测试组间差异的基础。主要扩展将是不仅允许测试不同组之间的不变性，而且还允许测试随年龄等连续变量而变化的组内的不变性。该方法将应用于验证性因子分析、潜在类别分析以及代表因子和潜在类别混合的模型，并且能够处理二元、序数和连续观察变量。该方法在评估人群中的药物滥用模式是否代表责任的持续变化或是否存在不同的群体方面应该被证明是有价值的。第二个目标是在增长曲线和其他因素混合模型的背景下扩展政权切换模型。这一目标旨在为涉及物质使用的开始和抵消的数据提供更好的模型，并帮助揭示异质性。第三，我们将开发分析某些形式的部分匿名数据的方法，例如涉及随机响应的数据。将比较这些方法在检测与预测变量、后遗症和部分随机数据的相关性的关系方面的性能，包括亲属和结果之间的相似性。所有模型开发都旨在允许分析和利用从亲属收集的数据，并将包括用于连锁和关联研究的遗传标记数据模型。应用数据分析将产生实质性结果，指导模型开发并测试稳健性。将收集和分析一系列横截面、纵向和遗传信息数据集。